Discover Awesome MCP Servers

mcp-numpy

An MCP server that exposes NumPy functionality as tools, enabling array creation, manipulation, mathematical operations, linear algebra, random sampling, statistics, and element-wise math through natural language.

pg-health

Archived MCP server for PostgreSQL health monitoring; functionality merged into pg-dash.

Mochify MCP

MCP implementation for connecting to the Mochify image processing API to convert, compress and resize images.

Hong Kong Transportation MCP Server

An MCP server providing access to Hong Kong transportation data, including passenger traffic statistics at control points and real-time bus arrival information for KMB and Long Win Bus services.

DateTime MCP Server

Provides timezone-aware date and time information with configurable time formats and timezone support. Enables users to get current date and time in their preferred timezone and format through simple MCP tools.

timergy

Create scheduling polls (like Doodle) from AI agents. Find the best time for meetings, dinners, and events. 5 tools: create_poll, get_poll, vote_on_poll, get_results, finalize_poll. No authentication required.

MCP DeepSeek 演示项目

Okay, here's a minimal example of using DeepSeek (assuming you mean DeepSeek LLM or a similar model) combined with MCP (MicroConfig Protocol) in a client-server scenario. This is a simplified illustration and would need adaptation based on your specific DeepSeek model and MCP implementation. **Conceptual Overview:** * **MCP (MicroConfig Protocol):** A lightweight protocol for configuration management. In this example, we'll use it to send a prompt to the DeepSeek server and receive the generated text. We'll assume a simple key-value pair structure for MCP messages. * **DeepSeek Server:** A server that hosts the DeepSeek LLM. It receives prompts via MCP, generates text, and sends the response back via MCP. * **Client:** A client that sends a prompt to the DeepSeek server via MCP and displays the response. **Simplified Code Examples (Python):** **1. DeepSeek Server (server.py):** ```python import socket import json # Assuming you have a way to interact with your DeepSeek model # (e.g., using a DeepSeek API or a local model) # Replace this with your actual DeepSeek interaction code. def generate_text_with_deepseek(prompt): """ Placeholder for DeepSeek model interaction. Replace with your actual DeepSeek API call or model inference. """ # Simulate DeepSeek response if "translate" in prompt.lower(): response = "Hola mundo!" # Example Spanish translation else: response = f"DeepSeek says: {prompt}" return response def handle_client(conn, addr): print(f"Connected by {addr}") try: while True: data = conn.recv(1024) # Receive up to 1024 bytes if not data: break try: # MCP: Assume JSON-based key-value pairs message = json.loads(data.decode('utf-8')) prompt = message.get("prompt") if prompt: generated_text = generate_text_with_deepseek(prompt) response_message = {"response": generated_text} conn.sendall(json.dumps(response_message).encode('utf-8')) else: error_message = {"error": "No prompt provided"} conn.sendall(json.dumps(error_message).encode('utf-8')) except json.JSONDecodeError: error_message = {"error": "Invalid JSON format"} conn.sendall(json.dumps(error_message).encode('utf-8')) except Exception as e: error_message = {"error": f"Server error: {str(e)}"} conn.sendall(json.dumps(error_message).encode('utf-8')) except ConnectionResetError: print(f"Connection reset by {addr}") finally: conn.close() print(f"Connection closed with {addr}") def start_server(): HOST = "127.0.0.1" # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Listening on {HOST}:{PORT}") while True: conn, addr = s.accept() handle_client(conn, addr) if __name__ == "__main__": start_server() ``` **2. Client (client.py):** ```python import socket import json HOST = "127.0.0.1" # The server's hostname or IP address PORT = 65432 # The port used by the server def send_prompt(prompt): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: try: s.connect((HOST, PORT)) message = {"prompt": prompt} s.sendall(json.dumps(message).encode('utf-8')) data = s.recv(1024) response = json.loads(data.decode('utf-8')) print(f"Received: {response}") except ConnectionRefusedError: print("Connection refused. Is the server running?") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": user_prompt = input("Enter a prompt for DeepSeek: ") send_prompt(user_prompt) ``` **How to Run:** 1. **Install Dependencies:** You'll need Python installed. No external libraries are strictly required for this minimal example, but you'll need to install the DeepSeek API client library if you're using a remote DeepSeek service. 2. **Replace Placeholder:** In `server.py`, **replace the `generate_text_with_deepseek` function with your actual DeepSeek model interaction code.** This is the crucial part where you integrate with the DeepSeek LLM. This will likely involve using an API key, setting model parameters, and handling the API response. 3. **Start the Server:** Run `python server.py` in a terminal. 4. **Run the Client:** Run `python client.py` in another terminal. The client will prompt you to enter text. Type a prompt and press Enter. The client will send the prompt to the server, the server will process it with DeepSeek, and the client will display the response. **Explanation:** * **MCP (Simplified):** The code uses JSON to encode and decode messages between the client and server. This is a simple form of MCP. A real MCP implementation might have more sophisticated features like versioning, error handling, and data validation. * **Sockets:** The code uses Python's `socket` module for network communication. The server listens for incoming connections, and the client connects to the server. * **Error Handling:** The code includes basic error handling for connection errors, JSON parsing errors, and server-side exceptions. * **DeepSeek Integration (Placeholder):** The `generate_text_with_deepseek` function is a placeholder. You **must** replace it with your actual DeepSeek API call or model inference code. This is the core of the integration. **Important Considerations:** * **DeepSeek API/Model:** This example assumes you have access to a DeepSeek LLM, either through an API or a local model. You'll need to obtain API credentials or set up your local model environment. * **Security:** This is a very basic example and does not include any security measures. In a production environment, you would need to implement authentication, authorization, and encryption. * **Scalability:** This example is not designed for high scalability. For production use, you would need to consider using a more robust server architecture and load balancing. * **MCP Implementation:** This example uses a very simple JSON-based MCP. A real MCP implementation might use a more efficient binary format or a more sophisticated protocol. * **Error Handling:** The error handling is basic. You should add more robust error handling and logging for production use. **Example Interaction:** 1. **Run `server.py`** 2. **Run `client.py`** 3. **Client Prompt:** `Translate "Hello world!" to Spanish` 4. **Client Output:** `Received: {'response': 'Hola mundo!'}` This minimal example provides a starting point for integrating DeepSeek with MCP. You'll need to adapt it to your specific requirements and environment. Remember to replace the placeholder code with your actual DeepSeek interaction logic. Good luck!

mcp-mcpmarket

Enables discovery and automatic installation of MCP servers via mcpmarket.com integration. Searches repositories, scrapes GitHub READMEs for configuration details, and provides one-click installation to MCP clients.

MCP Memory

Enables AI assistants to remember user information across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage and retrieval.

AgentVeil Protocol

Trust, identity, and reputation infrastructure for AI agents. Register agents with W3C DID (Ed25519), check EigenTrust reputation scores, submit peer attestations, search agents by capability, and verify IPFS-anchored audit trails. 11 tools.

Dewey

Enables searching and researching document collections through hybrid semantic search and agentic research queries with grounded, cited answers. It allows users to list collections, scan document sections, and retrieve full Markdown content via MCP-compatible agents.

vCluster YAML MCP Server

Enables AI assistants to query, validate, and create vCluster YAML configurations directly from GitHub, supporting version-specific queries and automatic validation.

vet-mcp

vet-mcp

OCI Core Services FastMCP Server

A dedicated server for Oracle Cloud Infrastructure (OCI) Core Services that enables management of compute instances and network operations with LLM-friendly structured responses.

linear-mcp

A Model Context Protocol server that enables interaction with Linear workspaces to manage issues, projects, cycles, and teams through a GraphQL interface. It provides 21 tools for comprehensive task management, search, and workflow coordination using OAuth2 authentication.

invinoveritas

A Lightning-paid tool stack for autonomous agents — capital-scale-aware second-opinion /review (Sentinel mode auto-injects live trading state), reasoning, structured decisions, sandboxed code execution, paid agent-to-agent messaging, persistent memory, and signed audit proofs. Built and used daily by our own agent fleet who pay each other in sats. Pay-per-use via Bearer balance or L402.

enigmagent-mcp

Local AES-256-GCM encrypted vault for AI agents. Resolve {{PLACEHOLDER}} secrets in prompts at runtime — LLMs never see real API keys. Argon2id key derivation, zero cloud.

Test MCP Feb4 MCP Server

An MCP server that provides standardized tools for AI agents to interact with the Test MCP Feb4 API. It enables LLMs to access API endpoints through asynchronous operations and standardized Model Context Protocol tools.

MBTA MCP Server

Enables AI clients to access Boston's MBTA public transit data, including real-time predictions, schedules, route planning, and service alerts.

Gmail MCP Server

Provides comprehensive Gmail integration with 25+ tools for intelligent email management, including AI-powered categorization, advanced search and filtering, automated archiving and cleanup, analytics, and secure OAuth2 authentication.

Pronounce / pronounce-mcp

Look up how engineers actually pronounce project / product / jargon names (kubectl, nginx, JSON, Pydantic, JWT) — 1452+ entries with IPA, respelling, source citation, and confidence level.

ralph-wiggum-mcp

An enhanced Model Context Protocol (MCP) server implementing the Ralph Wiggum technique for iterative, self-referential AI development loops.

bikky

Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.

Agent Sense

Provides AI agents with environmental sensing capabilities including current time, IP-based geolocation, system information, and hardware details. Enables more contextual and accurate AI responses based on user's environment.

Meraki MCP Server

Exposes a curated subset of the Cisco Meraki Dashboard API to MCP-aware clients with role-based access control.

Minecraft RCON MCP Server

Connects AI agents to Minecraft servers via RCON to execute commands, monitor logs, and perform read-only SQLite database queries. It is specifically designed to facilitate AI-assisted plugin development, live debugging, and automated testing workflows.

GIT MCP Server

Un servidor de Protocolo de Contexto de Modelo (MCP) para proporcionar herramientas de git para Agentes LLM, con correcciones para el problema de almacenamiento en caché del parámetro amend.

Swagger Testcase MCP

MCP server for API test case generation from Swagger/OpenAPI specs. Parses Swagger 2.0 and OpenAPI 3.x, generates test cases across 8 categories (positive, negative, boundary, auth, security, idempotency, pagination, business logic), and exports to Postman, TestRail, Allure, k6, pytest, Gherkin, and CSV. Supports internal corporate APIs with auth headers. Auto-saves export files to your working di

Code Graph Knowledge System

Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.